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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
Experience * Proficiency in analyzing functional magnetic resonance imaging data. * Demonstrated skill in manuscript preparation, managing large datasets, multivariate analytical approaches, and/or
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: Job Requirements: Required: PhD in computer science, biomedical informatics, electrical engineering, or a related field Demonstrated expertise in machine learning, deep learning, and image-based
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Substantial published experience with PET imaging analysis. Experience with EEG data analysis or digital signal processing. Working knowledge of scientific computing languages, including Matlab, Python
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vision for extracting complex patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms leading to new applications of machine learning and artificial intelligence
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social science to join us in advancing real-time analyses of political and health-related audiovisual and textual messages using computational methods. WMP leverages large-scale advertising and media
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big-picture questions. Preferred Qualifications: Experience with computational topology software (e.g., GUDHI, Ripser, giotto-tda, or similar). Familiarity with natural language processing (NLP
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experience unless stated elsewhere in the job posting. Skills: 3D Imaging, Collaboration, Communication, Computer Coding, Data Analysis, Data Interpretations, Data Science, Electroencephalogram (EEG
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, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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Science and Infrastructure, Imaging and Manipulation, and nanofabrication facilities. The postdoc will create thin film heterostructures combining nonlinear optical materials and superconductors and other
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(Thorlabs) for imaging large cortical and subcortical areas in awake head-fixed animals, two 2-photon microscopes for single-cell electrophysiological experiments in brain slices (Chun et al, Science 2014